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TF-FAS: Twofold-Element Fine-Grained Semantic Guidance for Generalizable Face Anti-spoofing

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15065))

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Abstract

Generalizable Face anti-spoofing (FAS) approaches have recently garnered considerable attention due to their robustness in unseen scenarios. Some recent methods incorporate vision-language models into FAS, leveraging their impressive pre-trained performance to improve the generalization. However, these methods only utilize coarse-grained or single-element prompts for fine-tuning FAS tasks, without fully exploring the potential of language supervision, leading to unsatisfactory generalization ability. To address these concerns, we propose a novel framework called TF-FAS, which aims to thoroughly explore and harness twofold-element fine-grained semantic guidance to enhance generalization. Specifically, the Content Element Decoupling Module (CEDM) is proposed to comprehensively explore the semantic elements related to content. It is subsequently employed to supervise the decoupling of categorical features from content-related features, thereby enhancing the generalization abilities. Moreover, recognizing the subtle differences within the data of each class in FAS, we present a Fine-Grained Categorical Element Module (FCEM) to explore fine-grained categorical element guidance, then adaptively integrate them to facilitate the distribution modeling for each class. Comprehensive experiments and analysis demonstrate the superiority of our method over state-of-the-art competitors. Code: https://github.com/xudongww/TF-FAS

X. Wang and K.-Y. Zhang—Equal contributions.

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Acknowledgements

This work was supported by National Science and Technology Major Project (No. 2022ZD0118201), the National Science Fund for Distinguished Young Scholars (No. 62025603), the National Natural Science Foundation of Chine a (No. U21B2037, No. U22B2051, No. U23A20383, No. 62176222, No. 62176223, No. 62176226, No. 62072386, No. 62072387, No. 62072389, No. 62002305 and No. 62272401), and the Natural Science Foundation of Fujian Province of China (No. 2022J06001).

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Authors and Affiliations

  1. Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University, Xiamen, China

    Xudong Wang, Pingyang Dai & Rongrong Ji

  2. Youtu Lab, Tencent, Shenzhen, China

    Xudong Wang, Ke-Yue Zhang, Taiping Yao & Shouhong Ding

  3. Shanghai Jiao Tong University, Shanghai, China

    Qianyu Zhou

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  1. Xudong Wang
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  2. Ke-Yue Zhang
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  3. Taiping Yao
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  4. Qianyu Zhou
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  5. Shouhong Ding
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  6. Pingyang Dai
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  7. Rongrong Ji
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Correspondence to Taiping Yao or Pingyang Dai .

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Editors and Affiliations

  1. University of Birmingham, Birmingham, UK

    Aleš Leonardis

  2. University of Trento, Trento, Italy

    Elisa Ricci

  3. Technical University of Darmstadt, Darmstadt, Germany

    Stefan Roth

  4. Princeton University, Princeton, NJ, USA

    Olga Russakovsky

  5. Czech Technical University in Prague, Prague, Czech Republic

    Torsten Sattler

  6. École des Ponts ParisTech, Marne-la-Vallée, France

    Gül Varol

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Wang, X. et al. (2025). TF-FAS: Twofold-Element Fine-Grained Semantic Guidance for Generalizable Face Anti-spoofing. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15065. Springer, Cham. https://doi.org/10.1007/978-3-031-72667-5_9

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